Visual tags for search results generated from social network information

ABSTRACT

Search results, including sponsored links and algorithmic search results, are generated in response to a query, and are marked based on frequency of clicks on the search results by members of social network who are within a predetermined degree of separation from the member who submitted the query. The markers are visual tags and comprise either a text string or an image.

RELATED APPLICATION

This application is a continuation-in-part of application Ser. No.10/867,610, filed Jun. 14, 2004, entitled “Method of Sharing SocialNetwork Information with Existing User Databases.”

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention generally relates to data processing, and moreparticularly, to a method and system for generating and presentingsearch results that are based on social network information.

2. Description of the Related Art

Search engines have become popular tools to identify and locate specificinformation on the Internet. A search engine is a computer program that,when queried for information, retrieves either related information orpointers to the location of related information, or both, by evaluatingcontent stored in its search database.

A key metric in evaluating the performance of search engines isrelevance of the search results. Search engine developers are alwaysstriving to deliver search results that are relevant to the search querybeing processed. Consistent with this goal, there have been attempts torank search results based on a number of different factors. One of themore popular ways to rank search results involves analyzing the locationand frequency of keywords on a web page. Another frequently usedtechnique is analyzing how web pages link to each other. A web page getsa ranking boost based on the number of other web pages that are linkedto it. Click-through rates of search results are analyzed in some searchengines. The general rule is: the higher the click-through rate, thehigher the ranking.

SUMMARY OF THE INVENTION

The invention provides still another technique to improve the relevanceof search results. According to an embodiment of the invention, searchresults, including sponsored links and algorithmic search results, aregenerated in response to a query, and are ranked based on the frequencyof clicks on the search results by members of social network who arewithin a predetermined degree of separation from the member whosubmitted the query. The predetermined degree of separation is equal toone if the click activities of only the friends of the member whosubmitted the query are to be examined.

A search result may also be marked based on its click history. In oneembodiment, a search result is marked with an image or a text string ifthere was a single click on the search result by a friend of the memberwho submitted the query. In other embodiments, the frequency of clicksby members of social network who are within a predetermined degree ofseparation from the member who submitted the query is examined. If suchfrequency exceeds a minimum value, the associated search result ismarked with an image or a text string.

BRIEF DESCRIPTION OF THE DRAWINGS

So that the manner in which the above recited features of the presentinvention can be understood in detail, a more particular description ofthe invention, briefly summarized above, may be had by reference toembodiments, some of which are illustrated in the appended drawings. Itis to be noted, however, that the appended drawings illustrate onlytypical embodiments of this invention and are therefore not to beconsidered limiting of its scope, for the invention may admit to otherequally effective embodiments.

FIG. 1 is a diagram illustrating the relationships between members in asocial network.

FIG. 2 is a block diagram illustrating a system for providingrelationship information from a social network to an existing database,according to one embodiment of the present invention.

FIG. 3 is a sample adjacency list that is maintained by the graphsservers of the present invention.

FIG. 4 is a block diagram illustrating the relationships between membersin a social network and the relationships between users in an existingdatabase.

FIG. 5 is a flow diagram illustrating a method for providingrelationship information to an existing database.

FIG. 6 is a flow diagram illustrating a method for using relationshipinformation obtained from a social network.

FIG. 7 is a flow diagram illustrating a method for carrying out a searchrequest using relationship information obtained from a social network.

FIG. 8 is a sample query and search results generated using relationshipinformation obtained from a social network.

FIG. 9 is a sample query, search results, and relevance markersgenerated using relationship information obtained from a social network.

FIG. 10 is a flow diagram illustrating another method for carrying out asearch request using relationship information obtained from a socialnetwork.

DETAILED DESCRIPTION

A social network is generally defined by the relationships among groupsof individuals, and may include relationships ranging from casualacquaintances to close familial bonds. A social network may berepresented using a graph structure. Each node of the graph correspondsto a member of the social network. Edges connecting two nodes representa relationship between two individuals. In addition, the degree ofseparation between any two nodes is defined as the minimum number ofhops required to traverse the graph from one node to the other. A degreeof separation between two members is a measure of relatedness betweenthe two members.

FIG. 1 illustrates a graph representation of a social network centeredon a given individual (ME). Other members of this social network includeA-U whose position, relative to ME's, is referred to by the degree ofseparation between ME and each other member. Friends of ME, whichincludes A, B, and C, are separated from ME by one degree of separation(1 d/s). A friend of a friend of ME is separated from ME by 2 d/s. Asshown, D, E, F and G are each separated from ME by 2 d/s. A friend of afriend of a friend of ME is separated from ME by 3 d/s. FIG. 1 depictsall nodes separated from ME by more than 3 degrees of separation asbelonging to the category All.

Degrees of separation in a social network are defined relative to anindividual. For example, in ME's social network, H and ME are separatedby 2 d/s, whereas in G's social network, H and G are separated by only 1d/s. Accordingly, each individual will have their own set of first,second and third degree relationships.

As those skilled in the art understand, an individual's social networkmay be extended to include nodes to an Nth degree of separation. As thenumber of degrees increases beyond three, however, the number of nodestypically grows at an explosive rate and quickly begins to mirror theALL set.

FIG. 2 is a block diagram illustrating a system for creating andmanaging an online social network. As shown, FIG. 2 illustrates a system100, including an application server 200 and graph servers 300. Thecomputers of system 100 are connected by a network 400, e.g., theInternet, and accessible by over the network by a plurality ofcomputers, collectively designated as 500. The application server 200manages a member database 210, a relationship database 220, and a searchdatabase 230.

The member database 210 contains profile information for each of themembers in the online social network managed by the system 100. Theprofile information may include, among other things: a unique memberidentifier, name, age, gender, location, hometown, references to imagefiles, listing of interests, attributes, and the like. The profileinformation also includes VISIBILITY and CONTACTABILITY settings, theuses of which are described in a commonly owned, co-pending application,“System and Method for Managing Information Flow Between Members of anOnline Social Network,” (Atty. Docket No. FRIE/0002), filed May 26,2004, the contents of which are hereby incorporated by reference. Therelationship database 220 stores information defining to the firstdegree relationships between members. The relationship database 220stores information relating to the first degree relationships betweenmembers. In addition, the contents of the member database 210 areindexed and optimized for search, and stored in the search database 230.The member database 210, the relationship database 220, and the searchdatabase 230 are updated to reflect inputs of new member information andedits of existing member information that are made through the computers500.

The application server 200 also manages the information exchangerequests that it receives from the remote computers 500. The graphservers 300 receive a query from the application server 200, process thequery and return the query results to the application server 200. Thegraph servers 300 manage a representation of the social network for allthe members in the member database. The graph servers 300 have adedicated memory device 310, such as a random access memory (RAM), inwhich an adjacency list that indicates all first degree relationships inthe social network is stored.

A sample adjacency list that reflects the social network map of FIG. 1is shown in FIG. 3. A list item is generated for each member andcontains a member identifier for that member and member identifier(s)corresponding to friend(s) of that member. As an alternative to theadjacency list, an adjacency matrix or any other graph data structuremay be used. The graph servers 300 and related components are describedin detail in a commonly owned, co-pending application, “System andMethod for Managing an Online Social Network,” (Atty. Docket No.FRIE/0003), filed May 26, 2004, the contents of which are herebyincorporated by reference.

The graph servers 300 respond to requests from application server 200 toidentify relationships and the degree of separation between members ofthe online social network. The application server 200 is furtherconfigured to process requests from a third party application 610 toprovide social network information (e.g., the relationships betweenindividuals) for user records maintained in a third party database 620.The third-party application 610 makes the requests to the applicationserver 200 through an application programming interface (API) 600.

The API 600 provides application developers with a set of methods,method signatures, data structures, and the like that expose aninterface used by the third party application 610 to communicate withthe application server 200. Application developers use the methodsdefined by the API 600 to construct applications that can communicatewith the application server 200. There are many programmatic andsyntactical choices to define the API methods that will effectivelyencapsulate the data and operations that are used in the invention.Thus, specific API methods, routines and data structures described beloware illustrative in nature and are neither limiting nor definitive ofthe API 600.

FIG. 4 illustrates an example of a subset of a social network graph 350maintained by the graph servers 300 along with a subset of databaserecords 360 maintained in the third party database 620. As depicted, thesubset 350 includes members A, B, C, D, E and F, and the subset 360include records for A′, B′, E′, F′, G and H. A and A′ represent the sameindividual but are labeled differently to signify that a database recordfor this person exists in both the member database 210 and the thirdparty database 620. The same is true for: B and B′, E and E′, and F andF′. By contrast, database records for individuals C, D exist in themember database 210, but not in the third party database 620, anddatabase records for individuals G, H exist in the third party database620, but not in the member database 210.

The relationships between individuals A, B, C, D, E and F are maintainedin the relationship database 220 and the graph servers 300. The flowdiagram shown in FIG. 5 is used to find out the relationships betweenindividuals A′, B′, E′, F′, G and H, namely to obtain the social networkinformation used to construct the edges (shown as dashed lines in FIG.4) between (A′, B′), (E′, F′) and (A′, F′).

FIG. 5 is a flow diagram that illustrates a method for processing arequest for social network information by the third party application610 in the system of FIG. 2. In Step 410, the application server 200receives a request from the third party application 610 to identifysocial network relationships (i.e., the edges between nodes) among userswho are represented by a set of ID tokens 405. For example, API 600 mayprovide a method to make such a request according to the following:

-   -   relationship_pairs[ ] find_Connections(ID_Tokens[ ],        credential_Type, hash_Type).

The find_Connections method accepts an array of ID Tokens, an indicationof the type of shared credentials used (credential_Type), and anindication of the type of hash algorithms used (hash_Type). In response,the find_Connections method returns an array of relationship pairscomprising two ID tokens and an indication of the relationship betweenthe members represented by the two ID tokens.

The shared credential types include an e-mail address(credential_Type=1), first and last name (credential_Type=2), telephonenumber (credential_Type=3), and any other types or a combination of twoor more types that might be used to identify an individual. Of the threetypes specifically identified here, the e-mail address type ispreferred, because in most instances an e-mail address is associatedwith a single individual.

The hash algorithm types include none (hash_Type=0), MD5 one-way hashalgorithm (hash_Type=1), and SHA-1 one-way hash algorithm (hash_Type=2).When hash_Type=1 or 2, the corresponding one-way hash algorithm is usedto create a hash value from the identifying information associated withthe credential type selected (e.g., e-mail, first and last name,telephone number, etc.), and the hash value is used as a sharedcredential. When hash_Type=0, a hash algorithm is not used and theshared credential comprises the identifying information associated withthe credential type selected (e.g., e-mail, first and last name,telephone number, etc.).

In Step 420, after receiving the set of the ID tokens 405 from the thirdparty application 610, the application server 200 compares the value ofeach ID token from the set against ID tokens corresponding to themembers of the online social network. The ID tokens corresponding to themembers of the online social network are generated using the sharedcredential type and the hash algorithm type specified in the variablescredential_Type and hash_Type. A match from this comparison indicatesthat there is a record for that individual in both the third partydatabase 620 and in the member database 210.

For some embodiments, the application server 200 may improve itsprocessing efficiency by generating the ID tokens for its members aheadof time and having them stored for use in the comparison of Step 420.For example, the application server 200 may maintain an index of uniquemember identifiers, each associated with the corresponding member'se-mail address (credential_Type=1, hash_Type=0), a hash value generatedfrom the corresponding member's e-mail address using the MD5 hashalgorithm (credential_Type=1, hash_Type=1), and a hash value generatedfrom the corresponding member's e-mail address using the SHA-1 hashalgorithm (credential_Type=1, hash_Type=2).

At this point, the application server 200 has identified which ID Tokenshave a member profile in the online social network. In Step 430, theapplication server 200 queries the graph servers 300 to obtain thespecific relationship information for the identified members. Forexample, referring to FIG. 4, for each member pair: (A, B), (A, E), (A,F), (B, E), (B, F), and (E, F), the application server 200 issues aquery to the graph servers 300 to obtain the degree of separationbetween the member pair.

Then, in Step 440, the application server 200 returns an array 455,which includes the ID tokens corresponding to each member pair and thedegree of separation obtained for each member pair, e.g., (A, B, 1), (A,E, 4), (A, F, 3), (B, E, 5), (B, F, 4), and (E, F, 1). Optionally, otherattributes, e.g., demographic information, may be returned. Using the IDtokens that are returned, the third party application 610 identifies thecorresponding members in the third party database 620, and records thedegrees of separation between the member pairs.

Note, in the above example, the application server 200 returns a pairindicating a third degree relationship between A and F, but does notinclude the connecting members, C and D. Unless the set of ID tokens 405includes a token for each member with a record in the online socialnetwork, the information returned by the application server 200 may beincomplete in some respects. In other words, when the relationship graphis reconstructed from the information returned by the application server200, A and F will be connected to two dummy nodes.

In another embodiment of the invention, the third party application 610may use a method from API 600 that requires the passing of a single IDtoken (e.g., corresponding to member M1), a shared credential type, ahash algorithm type, and a d/s setting N, in its request to theapplication server 200. In response, the application server 200 returnsan indication of M1's social network up to N degrees of separation. Themethod signature is as follows:

-   -   network get_Network(ID_Token, credential_Type, hash_Type, N)        The d/s setting N is optional. If it is omitted, a default        value, e.g., 3, is used. If it is specified, the application        server 200 returns an indication of M1's social network up to        the specified degree of separation.

After receiving the request according to the get_Network method, theapplication server 200 identifies the member corresponding to the IDtoken (e.g., M1) provided by the third party application 610. If the IDtoken does not correspond to any member, the application server returnsan indication of this to the third party application 610. Otherwise, theapplication server 200 queries the graph servers 300 to identify themembers of the online social network that are related to M1 within Ndegrees of separation (or a number specified in the get_Network method).For each member identified, the application server 200 creates an IDtoken in accordance with the shared credential type and the hashalgorithm type specified in the request. The application server 200returns all ID tokens so created along with an indication for each IDtoken the degree of separation from M1. The third party application 610then uses the returned set of ID tokens to determine whether the thirdparty database 620 contains records corresponding to the members in M1'ssocial network, and if there are, it stores the degree of separationinformation for each such record.

As an example, the third party application 610 may be an online gamingsite and the third party database 620 may be the database of registeredusers maintained by the online gaming site. The process described abovewould be used by the online gaming site to obtain social networkinformation for its registered users from the computer system of FIG. 2,so that each time a registered user logs in to play, the online gamingsite can invite (by e-mail or IM, for example) one or more additionalregistered users in his or her network to log on and play as well.

The present invention may also be used to clarify ambiguities in certainrequests for information. For example, if a user queried an onlinetelephone directory for the number of “John Smith,” many results may bereturned. The online telephone directory could use the get_Network APImethod to query the application server 200 to identify a John Smithpresent in the requestor's social network, likely eliminating all butone “John Smith” from consideration.

FIG. 6 illustrates the above process in further detail. In Step 510, theID token of the user requesting the number of “John Smith” (e.g., M1) istransmitted by the third party application 610 to the application server200 along with the get_Network request which also specifies the sharedcredential type used, the hash algorithm type used, and the d/s settingN. The application server 200 then compares the ID token of therequesting user with the ID tokens of its members. If a match is found,the application server 200 queries the graph server 300 for all membersrelated to the member corresponding to the matching ID token within Ndegrees of separation. The ID tokens of all such members are thentransmitted to the third party application 610. The third partyapplication 610 receives these ID tokens (Step 520) and compares themagainst the ID token of a “John Smith” candidate (Step 530). If there'sa match, it is confirmed that the “John Smith” candidate is the “JohnSmith” that M1 is looking for (Step 540). If there is not a match, the“John Smith” candidate is not confirmed as the “John Smith” that M1 islooking for (Step 550), and the third party application 610 compares thereceived ID tokens against the ID token of another “John Smith”candidate. This process is repeated until a match is found or all “JohnSmith” candidates have been exhausted.

The application server 200 may be configured to provide the degree ofseparation between two individuals. A method signature from the API 600call for this could be the following:

-   -   get_Degrees(ID_Token1, ID_Token2, credential_Type, hash_Type);        The application server 200 processes this call in a manner        similar to the above calls. First, the application server 200        resolves which members in the member database 210 correspond to        ID_Token1 and ID_Token2. If the application server 200 is unable        to resolve one or both an error is returned. Otherwise, the        application server 200 queries the graph servers 300 to        determine the degree of separation between the members        corresponding to ID_Token1 and ID_Token2. Once determined, the        application server 200 returns a number as the degree of        separation between the two members. Using the degree of        separation between the two individuals, the third party        application 610 may manage transaction processing based on the        relationship (or lack thereof) between the two members.

The third party application 610 may use this information to control thevisibility of information in the third party database 620. For example,the third party application 610 might store telephone numbers, or otherpersonal information related to a user M1 in the third party database620, and an access preference from M1 that specifies how closely relatedto M1 a user has to be (expressed in terms of degrees of separation) inorder to view M1's phone number. Using the relationship informationobtained from the online social network as described above, the thirdparty application 610 may limit access to M1's information stored in thethird party database 620 to only those users who are within N degrees ofseparation, where N is the degree of separation specified by M1 in theaccess preference.

In the above examples, as an alternative to the MD5 and SHA-1, MessageAuthentication Code (MAC) may be used as the hash algorithm. MAC is alsoa one-way hash algorithm but uses a secret key that the partymaintaining social network information and the party requesting socialnetwork information through the API 600 would agree to in advance. Theuse of the key provides extra security.

FIG. 2 depicts the third party database 620 to be external to thecomputer system 100 of the online social network. In alternativeembodiments of the invention, the operator of the online social networkmay act as an application service provider (ASP) that maintains thethird party database 620, on behalf of the third party, within thecomputer system 100. In such a case, the online social networkperiodically maps the social network information maintained by its graphservers 300 onto the third party database 620, so that the socialnetwork information will be made available for use without theinformation flow that is illustrated in FIGS. 5 and 6.

FIG. 7 is a flow diagram illustrating the steps carried out by a searchengine that uses social network information to tailor search resultsdelivered to its users in response to a search query. A sample searchquery 810 and search results 820, which includes sponsored links (onlineads) 830 and web search results 840, that are generated in response tothe sample search query 810 is illustrated in FIG. 8.

The sponsored links 830 represent hyperlinks to web pages of advertiserswho have agreed to pay the search engine operator for listing theirhyperlinks on the search results page of users who submit search queriesusing certain keywords. In a typical arrangement, the advertisers bid onkeywords such that higher bids result in higher placement on the searchresults page. The bids represent the amount the advertisers are willingto pay per click on their online ads. The web search results 840represent what is commonly known in the art as algorithmic searchresults.

In this example, the third party application 610 is a search engineoperator that manages a search results database, and the third partydatabase 620 represents a plurality of databases including: (i) a userdatabase that keeps track of all search queries specified by each userand, for each such search query, a record of all hyperlinks that theuser clicked on when search results responsive to the search query wereserved to the user; (ii) a database containing information onadvertisers, keywords that the advertisers bid on, and the sponsoredlinks corresponding to the keywords; and (iii) a database of web pagesthat are used to generate the algorithmic search results.

In Step 710, the third party application 610 receives a search queryfrom a registered user. In Step 720, the third party application 610retrieves the search results (both sponsored links and algorithmicsearch results) responsive to the search query from its search resultsdatabase. The retrieved sponsored links are ranked in accordance withthe bids submitted by their advertisers, and the retrieved algorithmicsearch results are ranked based on their relevance as determined by thesearch engine. In Step 730, the third party application 610 searches thethird party database 620 for search queries that match the one receivedfrom the user in Step 710. If there are no matches, the search resultsretrieved in Step 720 are served to the user (Steps 740 and 750).

If there are one or more matches, the algorithmic search results arere-ordered based on the frequency of “relevant” clicks on the hyperlinksassociated with the search results and then served to the user.Frequency of clicks is equal to the number of prior clicks on ahyperlink divided by the number of times that hyperlink was displayed,and hyperlinks with higher frequencies are ranked higher than hyperlinkswith lower frequencies. Relevant clicks are those clicks made by userswho are within a specified degree of separation from the user whorequested the search. The degree of separation information (i.e., socialnetwork or relationship information) may be maintained by the thirdparty application 610 or obtained from an online social network in themanner described above in connection with FIG. 5. The specified degreeof separation may be any number or set as ALL, in which case all clicksbecome relevant, and it may be set by the operator of the search engine,or it may be set by a user in his or her profile. For example, if theuser sets the specified degree of separation as 1, only clicks made bythose who are friends of the user become relevant clicks.

In addition, visual tags may be displayed on the search results pagenext to those search results (both sponsored links and algorithmicsearch results) for which relevant clicks have been recorded. FIG. 9shows a sample search query 910 and search results 920, which includessponsored links 930 and web search results 940, that are generated inresponse to the sample search query 910. Visual tags are displayed nextto those search results for which relevant clicks have been recorded.Visual tags may be an image 951 or a text string 952. When the visualtag is an image, the source for the web page that displays the searchresults 920 specifies a pointer to a file that contains that image. Whenthe visual tag is a text string, the source for the web page thatdisplays the search results 920 includes the text string.

The computer system 100 of the online social network may also deliverInternet search results to members of the online social network and toInternet users who are not members of the online social network. In thisexample, the computer system 100 is provided with an Internet searchresults database and an Internet search query database that keeps trackof all Internet search queries specified by each member of the onlinesocial network and, for each such search query, a record of allhyperlinks that the member clicked on when search results responsive tothe search query were served to the member.

When the computer system 100 receives an Internet search query from oneof its members, it retrieves the search results responsive to the searchquery from the Internet search results database, and searches theInternet search query database for search queries that match the onereceived from the member. If there are no matches, the search resultsretrieved from the Internet search results database are served to themember. If there is one or more matches, the search results retrievedfrom the Internet search results database are ranked based on thefrequency of “relevant” clicks on the hyperlinks associated with thesearch results and then served to the member. Relevant clicks are thoseclicks made by members who are within a specified degree of separationfrom the member who requested the search. The specified degree ofseparation may be any number or set as ALL, in which case all clicksbecome relevant, and it may be set by the operator of the online socialnetwork, or it may be set by a member in his or her profile. Forexample, if the member sets the specified degree of separation as 1,only clicks made by those who are friends of the members become relevantclicks.

When the computer system 100 receives an Internet search query from anInternet user who is not a member of the online social network, itretrieves the search results responsive to the search query from theInternet search results database, and searches the Internet search querydatabase for search queries that match the one received from the user.If there are no matches, the search results retrieved from the Internetsearch results database are served to the user. If there is one or morematches, the search results retrieved from the Internet search resultsdatabase are ranked based on the frequency of clicks on the hyperlinksassociated with the search results and then served to the user.

In a slightly different embodiment, the computer system 100 of theonline social network delivers both sponsored links and algorithmicsearch results to members of the online social network. This process isillustrated in FIG. 10. In this example, the computer system 100 isprovided with a search query database that keeps track of all Internetsearch queries specified by each member of the online social networkand, for each such search query, a record of all hyperlinks that themember clicked on when search results responsive to the search querywere served to the member. It also includes or is connected to: (i) adatabase containing information on advertisers, keywords that theadvertisers bid on, and the sponsored links corresponding to thekeywords; and (ii) a database of web pages that are used to generate thealgorithmic search results.

In Step 1001, the computer system 100 receives an Internet search queryfrom a member. In Step 1002, it retrieves records that are responsive tothe search query from the sponsored links database and the algorithmicsearch results database, and searches the Internet search query databasefor search queries that match the one received from the member.

In Steps 1003-1007, it processes each of the retrieved records insequence (Step 1003). In Step 1004, it computes the click-through rate(CTR) on that record by members of the social network who are within aspecified degree of separation from the member, and in Step 1005, checksto see if the computed CTR is greater than a minimum value. Thespecified degree of separation may be any number or set as ALL, and itmay be set by the operator of the search engine, or it may be set by auser in his or her profile. The minimum value may be zero or a smallvalue (e.g., 0.01). If the computed CTR is greater than the minimumvalue, the computer system 100 determines the record to be “relevant”and specifies a marker to be displayed next to this record in the searchresults (Step 1006). Flow then proceeds to Step 1007 where it continuesprocessing of the remaining records. If the computed CTR is less than orequal to the minimum value, flow proceeds to Step 1007 where itcontinues processing of the remaining records. After the last record hasbeen processed, the records are sorted based on their computed CTR (Step1008) and the content is transmitted for display.

While particular embodiments according to the invention have beenillustrated and described above, those skilled in the art understandthat the invention can take a variety of forms and embodiments withinthe scope of the appended claims.

1. A method of managing a query from a member of a social network, saidmethod comprising the steps of: retrieving records that are responsiveto the query; determining whether the records are relevant based onfrequency of clicks on hyperlinks associated with the records by membersof the social network; and preparing content to be transmitted fordisplay in response to the query, the content including the relevantrecords and specifying a marker that is to be displayed next to each ofthe relevant records.
 2. The method according to claim 1, wherein themarker comprises an image and the content includes a pointer to saidimage.
 3. The method according to claim 1, wherein the marker comprisesa text string that is specified in the content.
 4. The method accordingto claim 1, further comprising the step of computing for each record aclick-through rate of friends of the member for said record, wherein arecord is determined to be relevant if its associated click-through rateis larger than a minimum.
 5. The method according to claim 4, furthercomprising the step of ordering the records that are determined to berelevant based on the computed click-through rate so that the recordwith the highest computed click-through rate is to be displayed first.6. The method according to claim 1, wherein the records comprise onlineads.
 7. The method according to claim 1, wherein the relevance of eachrecord is determined based on the frequency of clicks by members of thesocial network who are within a predetermined degree of separation fromsaid member.
 8. The method according to claim 7, wherein thepredetermined degree of separation is
 1. 9. A method of preparingcontent to be displayed in response to a query, wherein the contentincludes first and second sets of results that are to be displayedpursuant to different display formats, said method comprising the stepsof: receiving the query from a member of a social network; retrievingrecords that are responsive to the query; determining whether therecords are relevant or non-relevant based on online activities ofmembers of the social network who are related to said member within apredetermined degree of separation; and including the relevant recordsin the first set of results and the non-relevant records in the secondset of results.
 10. The method according to claim 9, wherein each recordin the first set of results is to be displayed next to a marker.
 11. Themethod according to claim 10, wherein the marker comprises an image andthe content further includes a pointer to said image.
 12. The methodaccording to claim 10, wherein the marker comprises a text string andthe content further includes said text string.
 13. The method accordingto claim 9, wherein the predetermined degree of separation is specifiedby said member.
 14. The method according to claim 9, wherein thepredetermined degree of separation is
 1. 15. A method of generating asearch results page in response to a query from a member of a socialnetwork, comprising: retrieving records that are responsive to thequery; ordering the records on the search results page based onfrequency of clicks on hyperlinks associated with the records by othermembers of the network; and specifying an image to be displayed on thesearch results page next to at least one of the records.
 16. The methodaccording to claim 15, wherein the image is specified using an imagepointer.
 17. The method according to claim 16, wherein the other membersof the network are limited to those who are within a predetermineddegree of separation from said member.
 18. The method according to claim17, wherein the predetermined degree of separation is specified by saidmember.
 19. The method according to claim 15, wherein the records areassociated with web pages.
 20. The method according to claim 15, whereinthe other members of the network are limited to friends of said member.